Model Card: Persona-Driven Review & Rating Predictor

Model Details

• Developed by: Chisom Chibuike
• Model Type: Large Language Model (Autoregressive Transformer), fine-tuned via Parameter-Efficient Fine-Tuning (PEFT/QLoRA).
• Base Model: Llama 3.1 8B Instruct
• Language(s): English (primarily e-commerce review vernacular)
• License: Meta Llama 3.1 Community License
• Finetuning Date: May 2026

Intended Use

Primary Use Case

• Task A (Persona-to-Review Mapping): Predicting a user's likely numerical rating, and review text for a specific item, given their structured behavioral persona profile.
• Simulation & Testing: Enabling e-commerce platforms to simulate how different customer cohorts might react to new products or inventory changes.

Out-of-Scope Uses

• Generating fake reviews for real products to manipulate commercial ratings.
• Deploying without safety filtering to interact directly with end-users without monitoring for hallucinations.

Training Data & Inputs

• Input Context:
  • persona: A structured JSON object containing a user's values, writing style markers, common complaints, and rating distribution.
  • title: The textual headline of the product being review.
• Target Outputs:
  • average_rating: Numerical target (1.0 to 5.0).
  • text: The detailed review body reflecting the persona's tone, detail level, and vocabulary markers.

Evaluation Metrics

Limitations & Biases

Critical Caveat: The model's predictive power is strictly bounded by the data present in the training set (heavily weighted toward categories like All Beauty and Skincare in the initial samples).

• Domain Bias: Performance may degrade significantly if evaluated on items outside the primary training categories (e.g., items like Electronics or Automotive parts ).
• Cold Start Problem: The model requires a rich, established persona profile. If a persona contains minimal evidence or empty arrays, prediction accuracy drops.
• Style Homogenization: The model may default to generic "helpful reviewer" archetypes if the unique writing style signals in the input persona are weak.